PCB-Fire: Automated Classification and Fault Detection in PCB
This addresses quality control challenges for PCB manufacturers, but appears incremental as it combines existing pixel theory and object detection methods.
The authors tackled the problem of faulty component placement in printed circuit boards by developing an algorithm that detects missing components and classifies them, achieving optimized results on a given dataset.
Printed Circuit Boards are the foundation for the functioning of any electronic device, and therefore are an essential component for various industries such as automobile, communication, computation, etc. However, one of the challenges faced by the PCB manufacturers in the process of manufacturing of the PCBs is the faulty placement of its components including missing components. In the present scenario the infrastructure required to ensure adequate quality of the PCB requires a lot of time and effort. The authors present a novel solution for detecting missing components and classifying them in a resourceful manner. The presented algorithm focuses on pixel theory and object detection, which has been used in combination to optimize the results from the given dataset.